My Code and Outputs
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1. Loading and joining data
Set working directory first
Loading required libraries
library(sf)
library(rgdal)
library(raster)
library(mapview)
library(spatstat)
library(sp)
library(rgeos)
library(maptools)
library(GISTools)
library(tmap)
library(sf)
library(geojson)
library(geojsonio)
library(tmaptools)Opening shapefile, taken from ukdataservice.ac.uk, giving a map of Greater Manchester by 2011 English Census Merged Wards (hence data for basemap shapefile is by ward level).
require(rgdal)
require(ggplot2)
shp <- readOGR(dsn ="BoundaryData", layer="england_cmwd_2011", stringsAsFactors = F)## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\Tommy\Desktop\GIS_proj\Github\CASA0005GISProj\BoundaryData", layer: "england_cmwd_2011"
## with 215 features
## It has 4 fieldsplot(shp)
#checking Coordinate Reference System of the shapefile, which was originally NA
summary(shp)## Object of class SpatialPolygonsDataFrame
## Coordinates:
## min max
## x 351662.6 406087.2
## y 381165.4 421037.7
## Is projected: TRUE
## proj4string :
## [+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000
## +y_0=-100000 +datum=OSGB36 +units=m +no_defs +ellps=airy
## +towgs84=446.448,-125.157,542.060,0.1502,0.2470,0.8421,-20.4894]
## Data attributes:
## label altname name
## Length:215 Length:215 Length:215
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## code
## Length:215
## Class :character
## Mode :charactercrs(shp)## CRS arguments:
## +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000
## +y_0=-100000 +datum=OSGB36 +units=m +no_defs +ellps=airy
## +towgs84=446.448,-125.157,542.060,0.1502,0.2470,0.8421,-20.4894Specifying CRS of the greater manchester area shapefile as EPSG 4326 for latitude/longiture
Manchester <- spTransform(shp, CRS("+init=epsg:4326"))
st_crs(Manchester)## Coordinate Reference System:
## EPSG: 4326
## proj4string: "+proj=longlat +datum=WGS84 +no_defs"Plot Manchester basemap
plot(Manchester)
Extract just the city of Manchester itself rather than greater Manchester, as the area would otherwise be too big for analysis and Ripley’s K.
#extract the city
cityManchester <- subset(Manchester, name %in% c("Didsbury West","Fallowfield","Gorton North","Gorton South","Harpurhey","Higher Blackley","Hulme","Levenshulme","Longsight","Miles Platting and Newton Heath","Moss Side","Moston","Northenden","Old Moat","Rusholme","Sharston","Whalley Range","Withington","Woodhouse Park","Ancoats and Clayton","Ardwick","Baguley","Bradford","Brooklands","Burnage","Charlestown","Cheetham","Chorlton","Chorlton Park","City Centre","Crumpsall","Didsbury East"))
#Check to see that the correct borough has been pulled out
tm_shape(cityManchester) +
tm_polygons(col = NA, alpha = 0.5)
Read in police station data for greater manchester area, from the geoJSON file obtained using overpass-turbo.eu API and running a query for police stations in the Greater Manchester Area from OpenStreetMap.
Keeping only the points, as readOGR cannot read both points and polygons at the same time, and the points represent police stations.
pol <- rgdal::readOGR("C:/Users/Tommy/Desktop/GIS_proj/BoundaryData/polstn.geojson",require_geomType="wkbPoint")## OGR data source with driver: GeoJSON
## Source: "C:\Users\Tommy\Desktop\GIS_proj\BoundaryData\polstn.geojson", layer: "polstn"
## with 114 features;
## Selected wkbPoint feature type, with 51 rows
## It has 42 fieldsPlotting the police station points
plot(pol)
summary(pol)## Object of class SpatialPointsDataFrame
## Coordinates:
## min max
## coords.x1 -2.89431 -1.741055
## coords.x2 53.25742 53.778362
## Is projected: FALSE
## proj4string :
## [+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0]
## Number of points: 51
## Data attributes:
## id X.id amenity building
## node/1123287516: 1 node/1123287516: 1 police:51 office: 0
## node/1131472551: 1 node/1131472551: 1 yes : 0
## node/1290032691: 1 node/1290032691: 1 NA's :51
## node/1429962455: 1 node/1429962455: 1
## node/1865698140: 1 node/1865698140: 1
## node/233211757 : 1 node/233211757 : 1
## (Other) :45 (Other) :45
## type addr.postcode addr.street
## multipolygon: 0 OL7 0BQ : 1 Grappenhall Road: 1
## NA's :51 PR25 2EX: 1 Lancastergate : 1
## WA4 2AF : 1 Manchester Road : 1
## WA6 7NW : 1 Ship Street : 1
## BL1 8UN : 0 Spendmore Lane : 1
## (Other) : 0 (Other) : 1
## NA's :47 NA's :45
## contact.phone contact.website
## +44 161 872 5050: 0 http://www.gmp.police.uk: 0
## NA's :51 NA's :51
##
##
##
##
##
## name opening_hours
## 'B' Divisional Headquarters : 1 Mo-Sa 10:00-18:00: 0
## Adlington : 1 NA's :51
## Altrincham Police Station : 1
## Ashton under lyne Police Station: 1
## Bury Police Station : 1
## (Other) :19
## NA's :27
## note building.colour building.levels building.material
## Layout appoximate: 0 wheat: 0 2 : 0 brick: 0
## NA's :51 NA's :51 NA's:51 NA's :51
##
##
##
##
##
## height roof.colour roof.material roof.shape
## 4 : 0 grey: 0 gravel: 0 flat : 0
## NA's:51 NA's:51 NA's :51 round: 0
## NA's :51
##
##
##
##
## source addr.city
## OS_OpenData_StreetView: 2 Frodsham, Cheshire : 1
## OS OpenData StreetView: 1 Huddersfield : 1
## survey : 1 Leyland : 1
## Bing : 0 Manchester : 1
## Bing aerial image : 0 Stockton Heath, Warrington: 1
## (Other) : 0 (Other) : 0
## NA's :47 NA's :46
## phone fhrs.id operator
## +44 161 856 5629: 0 335284: 0 Cheshire Constabulary : 0
## +44 161 872 5050: 1 NA's :51 Cheshire Police : 2
## 0845 458 6392 : 0 Greater Manchester Police: 1
## NA's :50 Lancashire Constabulary : 1
## NA's :47
##
##
## wheelchair addr.housename source.building
## yes : 1 Atherton Police Station : 0 Bing: 0
## NA's:50 Cheshire Constabulary - Police Station: 1 NA's:51
## Police Headquarters : 0
## Stockton Heath Police Station : 1
## NA's :49
##
##
## source.geometry
## Bing: 0
## NA's:51
##
##
##
##
##
## designation
## Stockport Divisional Headquarters Greater Manchester Police: 0
## NA's :51
##
##
##
##
##
## roof.levels addr.housenumber
## 1 : 0 37 : 0
## NA's:51 NA's:51
##
##
##
##
##
## website
## http://www.gmp.police.uk : 0
## https://www.cheshire.police.uk/contact-us/police-stations-and-custody/northwich-police-station.aspx: 0
## NA's :51
##
##
##
##
## created_by is_in listed_status
## Potlatch 0.10b: 1 Fulwood: 1 Grade II: 2
## Potlatch 0.6a : 1 NA's :50 NA's :49
## NA's :49
##
##
##
##
## wikimedia_commons wheelchair.description
## File:Police Station, Warrington.jpg: 1 No Longer in use: 1
## NA's :50 NA's :50
##
##
##
##
##
## entrance
## yes : 1
## NA's:50
##
##
##
##
##
## description
## Opening Hours: Monday: 8am to 11pm Tuesday: 8am to 11pm Wednesday: 8am to 11pm Thursday: 8am to 11pm Friday: 8am to 11pm Saturday: 8am to 11pm Sunday: 8am to 11pm Bank Holidays: 8am to 11pm: 1
## NA's :50
##
##
##
##
##
## ele level HE_ref
## Street Level: 1 0 : 1 1240198: 1
## NA's :50 NA's:50 NA's :50
##
##
##
##
## Checking the CRS of the police station points - it is already EPSG4326!
st_crs(pol)## Coordinate Reference System:
## EPSG: 4326
## proj4string: "+proj=longlat +datum=WGS84 +no_defs"Removing duplicates from police station points
polstns <- remove.duplicates(pol)Plotting the police station points on top of the greater manchester area map using mapview
mapview::mapview(Manchester) + mapview::mapview(polstns, color = "white", col.regions = "black",legend=FALSE)Basemap of Manchester is a SpatialPolygonsDataFrame Police station points is a SpatialPointsDataFrame
class(cityManchester)## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"class(polstns)## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"Plot the police stations in the Greater Manchester area using tmap
tmap_mode("view")
tm_shape(Manchester) +
tm_polygons(col = NA, alpha = 0.5) +
tm_shape(polstns) +
tm_dots(col = "blue")tmap_mode("view")
tm_shape(cityManchester) +
tm_polygons(col = NA, alpha = 0.5) +
tm_shape(polstns) +
tm_dots(col = "blue")Filtering for police stations which are within the Manchester city boundary
proj4string(cityManchester) <- CRS("+init=epsg:4326")
proj4string(polstns) <- CRS("+init=epsg:4326")
polsub <- polstns[cityManchester,]
#checking to see that they've been removed
tmap_mode("view")
tm_shape(cityManchester) +
tm_polygons(col = NA, alpha = 0.5) +
tm_shape(polsub) +
tm_dots(col = "blue")Reading in the dataset for population density
library(readr)
popdens <- read_csv("popdens.csv")
summary(popdens)## geography popdens
## Length:32 Min. : 12.00
## Class :character 1st Qu.: 37.40
## Mode :character Median : 47.95
## Mean : 52.83
## 3rd Qu.: 66.53
## Max. :116.70Joining first dataset of population density with the Manchester basemap
require(sp)
?sp::merge
manchester_popdens <- merge(cityManchester, popdens, by.x = "name", by.y = "geography",duplicateGeoms = TRUE)Plotting map to test if join has succeeded
tmap_mode("view")
tm_shape(manchester_popdens) +
tm_polygons(col = NA, alpha = 0.5) +
tm_shape(polsub) +
tm_dots(col = "blue")Reading in the dataset for deprivation - the average number (out of 4 - employment, education, health and disability and household overcrowding) of dimensions a household was deprived in was used
depr <- read_csv("deprivation.csv")
summary(depr)## geography deprivation
## Length:32 Min. :0.5989
## Class :character 1st Qu.:0.9484
## Mode :character Median :1.1398
## Mean :1.0764
## 3rd Qu.:1.2323
## Max. :1.4070Joining second dataset of deprivation with the Manchester basemap
manchester_2 <- merge(manchester_popdens, depr, by.x = "name", by.y = "geography",duplicateGeoms = TRUE)Plotting map to check if second join has succeeded
tmap_mode("view")
tm_shape(manchester_2) +
tm_polygons(col = NA, alpha = 0.5) +
tm_shape(polsub) +
tm_dots(col = "blue")ses <- read_csv("SES.csv")
summary(ses)## geography socialgrade
## Length:32 Min. :1.739
## Class :character 1st Qu.:2.404
## Mode :character Median :2.744
## Mean :2.620
## 3rd Qu.:2.896
## Max. :3.119Joining third dataset of SES with the Manchester basemap
manchester_merged <- merge(manchester_2, ses, by.x = "name", by.y = "geography",duplicateGeoms = TRUE)Plotting map to check if third join has succeeded
tmap_mode("view")
tm_shape(manchester_merged) +
tm_polygons(col = NA, alpha = 0.5) +
tm_shape(polsub) +
tm_dots(col = "blue")manchester_merged is the map of Manchester city with merged data on population density, social grade and SES.
2. Plotting/performing KDE for crime data in Manchester city
Next step would be to map the crime datapoints onto the Manchester basemap. Crime data for June 2019 for the Greater Manchester area was used. This was taken from https://data.police.uk/data/
crime <- read_csv("manchester_crime.csv")
crime_locations <- st_as_sf(crime, coords = c("Longitude","Latitude"), crs = 4326)
class(crime)## [1] "spec_tbl_df" "tbl_df" "tbl" "data.frame"require(sf)
crime_sf <- st_as_sf(x = crime,
coords = c("Longitude", "Latitude"),
crs = "+init=epsg:4326")
# simple plot
plot(crime_sf)
crime_spdf <- as(crime_sf, "Spatial")
manchester_crime_test<-ggplot(crime, aes(x=Longitude,y=Latitude))+geom_point()+coord_equal()
manchester_crime_test
Plotting a 2D KDE of crime occurences in Manchester using the longitude and latitude data in the crime dataset
manchester_crime_test<-ggplot(crime, aes(x=Longitude,y=Latitude))+stat_bin2d(bins=30)
manchester_crime_test
Plotting a continuous distribution instead
manchester_crime_test+stat_density2d(aes(fill = ..level..), geom="polygon")
Plotting the crime datapoints to test
plot(crime_locations)
Examining metadata of the crime datapoints
summary(crime_locations)## geometry
## POINT :8828
## epsg:4326 : 0
## +proj=long...: 0Checking CRS of crime datapoints
crs(crime_locations)## [1] "+proj=longlat +datum=WGS84 +no_defs"Checking type of data of the crimem locations - sf, tbl_df, tbl, data.frame
class(crime_locations)## [1] "sf" "tbl_df" "tbl" "data.frame"Ensuring that crime_locations has EPSG 4326 as CRS
crime_locations_sp <- as(crime_locations, 'Spatial')
crime_locations_sp <- spTransform(crime_locations_sp, CRS("+init=epsg:4326"))Checking new CRS of crime datapoints and data type, removing duplicates
crs(crime_locations)## [1] "+proj=longlat +datum=WGS84 +no_defs"class(crime_locations)## [1] "sf" "tbl_df" "tbl" "data.frame"Plotting crime data points on Manchester basemap
mapview::mapview(manchester_merged) + mapview::mapview(crime_locations, color = "white", col.regions = "black",legend=FALSE, title = "Crime occurences in Manchester city")3. Descriptive statistics for data
library(tidyverse)
library(downloader)
library(rgdal)
library(sf)
library(ggplot2)
library(reshape2)
library(plotly)
library(highcharter)Histograms for each of the scale variables - population densities, deprivation, and SES Starting with popdens
# Histogram with mean line (red) and median line (blue) and density plot
ggplot(popdens, aes(x=popdens)) +
geom_histogram(aes(y=..density..), colour="black", fill="white", binwidth=10)+
geom_density(alpha=.2, fill="#FF6666") + geom_vline(aes(xintercept=mean(popdens)),
color="red", linetype="dashed", size=1) + geom_vline(aes(xintercept=median(popdens)),
color="blue", linetype="dashed", size=1) 
# Descriptive statistics
print(max(popdens$popdens))## [1] 116.7print(min(popdens$popdens))## [1] 12print(median(popdens$popdens))## [1] 47.95print(sd(popdens$popdens))## [1] 22.6814print(IQR(popdens$popdens))## [1] 29.125print(quantile(popdens$popdens, c(.25, .75)))## 25% 75%
## 37.400 66.525Plotting histogram for deprivation next
# Histogram with mean line (red) and median line (blue) and density plot
ggplot(depr, aes(x=deprivation)) +
geom_histogram(aes(y=..density..), colour="black", fill="white", binwidth= 0.1)+
geom_density(alpha=.2, fill="#FF6666") + geom_vline(aes(xintercept=mean(deprivation)),
color="red", linetype="dashed", size=1) + geom_vline(aes(xintercept=median(deprivation)),
color="blue", linetype="dashed", size=1) 
# Descriptive statistics
print(max(depr$deprivation))## [1] 1.406953print(min(depr$deprivation))## [1] 0.5988858print(median(depr$deprivation))## [1] 1.139765print(sd(depr$deprivation))## [1] 0.2171107print(IQR(depr$deprivation))## [1] 0.283867print(quantile(depr$deprivation, c(.25, .75)))## 25% 75%
## 0.9484226 1.2322897Plotting histogram for social grade next
# Histogram with mean line (red) and median line (blue) and density plot
ggplot(ses, aes(x=socialgrade)) +
geom_histogram(aes(y=..density..), colour="black", fill="white", binwidth= 0.1)+
geom_density(alpha=.2, fill="#FF6666") + geom_vline(aes(xintercept=mean(socialgrade)),
color="red", linetype="dashed", size=1) + geom_vline(aes(xintercept=median(socialgrade)),
color="blue", linetype="dashed", size=1) 
# Descriptive statistics
print(max(ses$socialgrade))## [1] 3.118662print(min(ses$socialgrade))## [1] 1.738781print(median(ses$socialgrade))## [1] 2.744045print(sd(ses$socialgrade))## [1] 0.3916912print(IQR(ses$socialgrade))## [1] 0.4920259print(quantile(ses$socialgrade, c(.25, .75)))## 25% 75%
## 2.403788 2.895813Plotting histogram for crimes recorded per ward next
#reading in data for crimes recorded per ward (taken from later part of the code after performing count)
library(readr)
crime_ward <- read_csv("crime_ward.csv")
summary(crime_ward)## geography crimes
## Length:32 Min. : 96.0
## Class :character 1st Qu.: 145.2
## Mode :character Median : 183.0
## Mean : 275.7
## 3rd Qu.: 330.8
## Max. :1887.0crime_ward$lnCrime <- log1p(crime_ward$crimes)
# Histogram with mean line (red) and median line (blue) and density plot
ggplot(crime_ward, aes(x=lnCrime)) +
geom_histogram(aes(y=..density..), colour="black", fill="white", binwidth= 0.05)+
geom_density(alpha=.2, fill="#FF6666") + geom_vline(aes(xintercept=mean(lnCrime)),
color="red", linetype="dashed", size=1) + geom_vline(aes(xintercept=median(lnCrime)),
color="blue", linetype="dashed", size=1) 
# Descriptive statistics
print(max(crime_ward$crimes))## [1] 1887print(min(crime_ward$crimes))## [1] 96print(median(crime_ward$crimes))## [1] 183print(sd(crime_ward$crimes))## [1] 313.0229print(IQR(crime_ward$crimes))## [1] 185.5print(quantile(crime_ward$crimes, c(.25, .75)))## 25% 75%
## 145.25 330.75Converting both cityManchester and crime_locations to data.frames to run the KDE, since it requires numeric input
df_cityManchester <- as.data.frame(cityManchester)
df_crime_locations <- as.data.frame(crime_locations)Create two separate fields for latitude and longitude from the manchester spatialpolygonsdataframe
polys = attr(cityManchester,'polygons')
npolys = length(polys)
for (i in 1:npolys){
poly = polys[[i]]
polys2 = attr(poly,'Polygons')
npolys2 = length(polys2)
for (j in 1:npolys2){
#do stuff with these values
coords = coordinates(polys2[[j]])
}
}
coords_df <- as.data.frame(coords)Plotting out crime occurences in Manchestaer city
ggplot() + geom_polygon(data = coords_df, aes(x = V1, y = V2), fill = "grey75") +
geom_point(data = crime, aes(x = Longitude, y = Latitude),
col = "dodger blue", alpha = .5, size = 1.5) +
coord_equal() +
ggtitle("Crime occurences in Manchester")
Counting number of crime occurence points per polygon
class(manchester_merged)## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"class(crime_spdf)## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"proj4string(manchester_merged)## [1] "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"crime_spdf <- spTransform(crime_spdf, CRS("+init=epsg:4326"))
proj4string(crime_spdf)## [1] "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"plot(manchester_merged)
plot(crime_spdf, col="red" , add=TRUE)
res <- over(crime_spdf, manchester_merged)
table(res$name)##
## Ancoats and Clayton Ardwick
## 484 368
## Baguley Bradford
## 179 333
## Brooklands Burnage
## 136 149
## Charlestown Cheetham
## 176 425
## Chorlton Chorlton Park
## 108 137
## City Centre Crumpsall
## 1887 270
## Didsbury East Didsbury West
## 101 126
## Fallowfield Gorton North
## 151 298
## Gorton South Harpurhey
## 212 443
## Higher Blackley Hulme
## 355 213
## Levenshulme Longsight
## 199 187
## Miles Platting and Newton Heath Moss Side
## 334 219
## Moston Northenden
## 148 171
## Old Moat Rusholme
## 136 172
## Sharston Whalley Range
## 151 129
## Withington Woodhouse Park
## 96 330After plotting number of crimes per ward on a .csv file, time to merge with the manchester_merged basemap. Reading in the dataset for crimes per ward using code chunk directly above
summary(crime_ward)## geography crimes lnCrime
## Length:32 Min. : 96.0 Min. :4.575
## Class :character 1st Qu.: 145.2 1st Qu.:4.985
## Mode :character Median : 183.0 Median :5.215
## Mean : 275.7 Mean :5.380
## 3rd Qu.: 330.8 3rd Qu.:5.804
## Max. :1887.0 Max. :7.543Joining crimes per ward dataset with the Manchester basemap
manchester_merged_final <- merge(x= manchester_merged, y=crime_ward, by.x = "name", by.y = "geography")Plotting map to check if join has succeeded.
library(tmap)
tmap_mode("view")
tm_shape(manchester_merged_final) +
tm_polygons(col = NA, alpha = 0.5) +
tm_shape(polsub) +
tm_dots(col = "blue")4. Choropleth maps by variables across Manchester
Making a choropleth map by crimes
library(ggplot2)
library(RColorBrewer)
library(classInt)
names(manchester_merged_final)## [1] "name" "label" "altname" "code" "popdens"
## [6] "deprivation" "socialgrade" "crimes" "lnCrime"#spplot(manchester_merged_final, "crimes")
# quantile breaks
breaks_qt <- classIntervals(manchester_merged_final$crimes, n = 7, style = "quantile")
br <- breaks_qt$brks
offs <- 0.0000001
br[1] <- br[1] - offs
br[length(br)] <- br[length(br)] + offs
# categories for choropleth map
manchester_merged_final$crimes_bracket <- cut(manchester_merged_final$crimes, br)
# plot
#my.palette <- brewer.pal(n = 7, name = "OrRd")
#spplot(manchester_merged_final, "crimes_bracket", col.regions=my.palette, main = "Manchester City Recorded Crimes in June 2019 by Ward")
#Using tmap
library(tmap)
tm_shape(manchester_merged_final) +
tm_polygons("crimes",
style="quantile",
title="Manchester city \nrecorded crimes \nby ward in June 2019")tmap_mode("view")Making a choropleth map by popdens
# quantile breaks
breaks_qt <- classIntervals(manchester_merged_final$popdens, n = 7, style = "quantile")
br <- breaks_qt$brks
offs <- 0.0000001
br[1] <- br[1] - offs
br[length(br)] <- br[length(br)] + offs
# categories for choropleth map
manchester_merged_final$popdens_bracket <- cut(manchester_merged_final$popdens, br)
# plot
#spplot(manchester_merged_final, "popdens_bracket", col.regions=my.palette, main = "Manchester City Population Density by Ward")
#Using tmap
library(tmap)
tm_shape(manchester_merged_final) +
tm_polygons("popdens",
style="quantile",
title="Manchester city \nPopulation Density \nby ward")tmap_mode("view")Making a choropleth map by deprivation
#spplot(manchester_merged_final, "deprivation")
breaks_qt <- classIntervals(manchester_merged_final$deprivation, n = 7, style = "quantile")
br <- breaks_qt$brks
offs <- 0.0000001
br[1] <- br[1] - offs
br[length(br)] <- br[length(br)] + offs
# categories for choropleth map
manchester_merged_final$deprivation_bracket <- cut(manchester_merged_final$deprivation, br)
# plot
#spplot(manchester_merged_final, "deprivation_bracket", col.regions=my.palette, main = "Manchester City deprivation by Ward")
#Using tmap
library(tmap)
tm_shape(manchester_merged_final) +
tm_polygons("deprivation",
style="quantile",
title="Manchester city \ndeprivation \nby ward")tmap_mode("view")Making a choropleth map by social grade
#spplot(manchester_merged_final, "socialgrade")
breaks_qt <- classIntervals(manchester_merged_final$socialgrade, n = 7, style = "quantile")
br <- breaks_qt$brks
offs <- 0.0000001
br[1] <- br[1] - offs
br[length(br)] <- br[length(br)] + offs
# categories for choropleth map
manchester_merged_final$socialgrade_bracket <- cut(manchester_merged_final$socialgrade, br)
# plot
#spplot(manchester_merged_final, "socialgrade_bracket", col.regions=my.palette, main = "Manchester City social grade by Ward")
#Using tmap
library(tmap)
tm_shape(manchester_merged_final) +
tm_polygons("socialgrade",
style="quantile",
title="Manchester city \nsocial grade \nby ward")tmap_mode("view")